Groups Of Experimental Data Research Articles

A precise feature extraction method for shock wave signal with improved CEEMD-HHT

Efficient extraction of feature parameters is the key to evaluating weapon damage performance. At present, many classical feature extraction algorithms have the problem that the extraction cannot meet the actual needs. A precise feature extraction method based on improved complementary ensemble empirical mode decomposition (CEEMD) with Hilbert-Huang Transform (HHT) was proposed in this paper to solve problems such as large noise and difficulties in extracting features of shockwave overpressure signals in complex test environment. We introduced CEEMD to decompose original explosion shockwave signals and adopted wavelet packet threshold de-noising to extract useful information from noisy high-frequency intrinsic mode functions (IMFs). The correlation coefficient algorithm is introduced to remove unrelated IMFs. In addition, we performed reconstruction of original signals to extract true time-course feature and utilized Hilbert-Huang Transform (HHT) to achieve precise extraction of instantaneous feature and energy spectrum of the various IMFs. The improved CEEMD-HHT is a precise method for shock wave signal analysis. It not only effectively removes noise, but also retains effective high-frequency information without losing useful information. Additionally, it overcomes the problems of mode mixing in empirical mode decomposition (EMD), and has the advantages of feature extraction with high accuracy and self-adaptation. The effectiveness of the proposed method is demonstrated by 2 groups of experimental data, and it precisely extracts instantaneous feature and energy spectrum of shockwave overpressure signal, which provide new theoretical basis for the evaluation of weapon damage.

Study of the Phase Boundary for C6F6 and SF6 under Microgravity

Two groups of experimental data obtained in the vicinity of the critical point are discussed. Group I describes the level ht of the meniscus separating the two phases of the substance in the cell. The measurements were performed for SF6 under the condition (g = 9.8 m s–2) during an experiment conducted in a space laboratory. Group II includes data on the density of liquid and vapor measured for C6F6 along the saturation curve under terrestrial condition. In both cases, the studied two-phase sample is located in a horizontal cylindrical cell. In the second experiment, the gravitational effect was also measured along the isotherms as the dependence of the sample density on the height h measured from the bottom of the cell. An equation relating the ht level (experiment I) with such functions as the order parameter fs and the average diameter fd is derived in this work. The obtained equation describes the initial experimental data at relative temperatures τ = (T – Tc)/Tc = 2 × 10–6–0.01. An approach is considered that takes into account the influence under microgravity (g = gM ⪡ 9.8 m s–2) on the height h (experiment II). The dependences that represent fs and fd and the density of the liquid and gas phases along the saturation curve of these substances are obtained. These dependences agree satisfactorily with the results of experiments I and II in a wide temperature range and correspond to the scaling theory of critical phenomena.

A Vector Line Simplification Algorithm Based on the Douglas–Peucker Algorithm, Monotonic Chains and Dichotomy

When using the traditional Douglas–Peucker (D–P) algorithm to simplify linear objects, it is easy to generate results containing self-intersecting errors, thus affecting the application of the D–P algorithm. To solve the problem of self-intersection, a new vector line simplification algorithm based on the D–P algorithm, monotonic chains and dichotomy, is proposed in this paper. First, the traditional D–P algorithm is used to simplify the original lines, and then the simplified lines are divided into several monotonic chains. Second, the dichotomy is used to search the intersection positions of monotonic chains effectively, and intersecting monotonic chains are processed, thus solving the self-intersection problems. Two groups of experimental data are selected based on large data sets. Results demonstrate that the proposed experimental method has advantages in algorithmic efficiency and accuracy when compared to the D–P algorithm and the Star-shaped algorithm.

Model updating for real time dynamic substructures based on UKF algorithm

Combining the advantages of numerical simulation with experimental testing, real-time dynamic substructure (RTDS) testing provides a new experimental method for the investigation of engineered structures. However, not all unmodeled parts can be physically tested, as testing is often limited by the capacity of the test facility. Model updating is a good option to improve the modeling accuracy for numerical substructures in RTDS. In this study, a model updating method is introduced, which has great performance in describing this nonlinearity. In order to determine the optimal parameters in this model, an Unscented Kalman Filter (UKF)-based algorithm was applied to extract the knowledge contained in the sensors data. All the parameters that need to be identified are listed as the extended state variables, and the identification was achieved via the step-by-step state prediction and state update process. Effectiveness of the proposed method was verified through a group of experimental data, and results showed good agreement. Furthermore, the proposed method was compared with the Extended Kalman Filter (EKF)-based method, and better accuracy was easily found. The proposed parameter identification method has great applicability for structural objects with nonlinear behaviors and could be extended to research in other engineering fields.

High Accuracy When Measuring Physical Constants: From the Perspective of the Information-Theoretic Approach

The practical value of high-precision models of the studied physical phenomena and technological processes is a decisive factor in science and technology. Currently, numerous methods and criteria for optimizing models have been proposed. However, the classification of measurement uncertainties due to the number of variables taken into account and their qualitative choice is still not given sufficient attention. The goal is to develop a new criterion suitable for any groups of experimental data obtained as a result of applying various measurement methods. Using the “information-theoretic method”, we propose two procedures for analyzing experimental results using a quantitative indicator to calculate the relative uncertainty of the measurement model, which, in turn, determines the legitimacy of the declared value of a physical constant. The presented procedure is used to analyze the results of measurements of the Boltzmann constant, Planck constant, Hubble constant and gravitational constant.

Surrogate Models for Performance Prediction of Axial Compressors Using through-Flow Approach

Two-dimensional design and analysis issues on the meridional surface, which is important in the preliminary design procedure of compressors, are highly dependent on the accuracy of empirical models, such as the prediction of total pressure loss model and turning flow angle. Most of the widely used models are derived or improved from experimental data of some specific cascades with low-loading and low-speed airfoil types. These models may work for most conventional compressors but are incapable of accurately estimating the performance for some specific cases like transonic compressors. The errors made by these models may mislead the final design results. Therefore, surrogate models are developed in this work to reduce the errors and replace the conventional empirical models in the through-flow calculation procedure. A group of experimental data considering a two-stage transonic compressor is used to generate the airfoils database for training the surrogate models. Sensitivity analysis is applied to select the most influential features. Two supervised learning approaches including support vector regression (SVR) and Gaussian process regression (GPR) are used to train the models with a Bayesian optimization algorithm to obtain the optimal hyper parameters. The trained models are integrated into the through-flow code based on streamline curvature method (SLC) to predict the overall performance and internal flow field of the transonic compressor on five rotational speed lines for validation. The predictions are compared with the experimental data and the results of conventional empirical models. The comparison shows that SVR and GPR respectively reduce the predicted error of empirical models by 62.2% and 55.2% for the total pressure ratio and 48.4% and 50.1% for adiabatic efficiency on average. This suggests that the surrogate models constitute an alternative way to predict the performance of airfoils in through-flow calculation where empirical models are inefficient.

The thermal effects of holmium laser lithotripsy in urinary calculus in an vitro model

Objective The purpose of this study was to investigate the factors influencing the thermal effects of holmium laser lithotripsy of urinary calculi and its safety. Methods We constructed an in vitro model of holmium laser thermal effect, then studied the affected factors about thermal effects of holmium laser lithotripsy: different power and energy patterns (1.0 J×10 hz, 0.5 J×20 hz, 1.0 J×20 hz, 2.0 J×20 hz), the perfusion fluid temperature (20 ℃ physiological saline, 37 ℃ physiological saline), the infusion speed (0 ml/min, 10 ml/min, 16 ml/min), optical fiber diameter (200, 365, 550 μm dahua holmium laser optical fiber), gravel model (gravel group and empty group). The holmium laser stimulated 120 s, during which the electronic thermometer measured and recorded temperature inside and outside the model each second. Each group experiments were repeated 3 times, and the average value of the three groups of experimental data was taken as the final experimental result. Independent sample T test or One-way ANOVA was used for comparison between groups. The optimal scale regression method was used to construct the influencing factor model of holmium laser thermal effect, and P<0.05 was considered as statistically significant difference. Results As the test begins, the temperature in the test tube increased rapidly, at around the 40 s, the temperature reached the plateau stage, and then the temperature tend to be stable. The results of optimal scale regression analysis showed that power and energy mode (F=18.533, P<0.05), perfusion velocity (F=220.461, P<0.01; F=495.783, P<0.01; F=32.287, P<0.01; F=429.237, P<0.01), perfusion liquid temperature (t=-4.657, P<0.01; t=-4.91, P<0.01; t=-4.157, P<0.05; t=-2.933, P<0.05) and fiber diameter (F=15.88, P<0.01; F=137.053, P<0.01; F=413.411, P<0.01; F=106.437, P<0.01) were the influencing factors of the thermal effect of holmium laser, and the difference was statistically significant (F=228.857, P<0.01). Conclusion The power and energy of holmium laser, perfusion rate, perfusion temperature and fiber diameter were the influencing factors of the thermal effect of holmium laser. Reducing the power of holmium laser, increasing the perfusion speed, using fiber with small diameter and choosing normal temperature perfusion solution were all helpful to reduce the thermal effects of holmium laser. Key words: Holmium laser; Thermal effect; Model in vitro

Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

A unified poroviscoelastic model with mesoscopic and microscopic heterogeneities

The wave-induced fluid flow (WIFF) is considered to be the main cause of dispersion and attenuation of seismic waves in fluid-saturated porous media. Among numerous theories, the mesoscopic and microscopic heterogeneities are considered to be the primary mechanisms causing the WIFF. Furthermore, in most rocks, the mesoscopic and microscopic heterogeneities exist simultaneously and can cause obvious transitions of the fast P-wave velocity, which means it is necessary to consider the influence of the two mechanisms on the dispersion and attenuation simultaneously. Numerous results have shown that the dispersions and attenuations caused by these two mechanisms can be approximated in terms of the Zener model. To combine the two mechanisms into a unified model, we introduce a new generalized Zener model into the Biot poroelasticity theory to obtain a new poroviscoelastic model. Comparisons between the numerical results and two groups of experimental data further confirm the validity of our new model.

Research on Impact Performance of Hydraulic Rock Drill with Floating Characteristics of Double Damping System

As a technological innovation of high‐power hydraulic rock drill, double damping system has a very important effect on impact performance. The double damping system is a floating mechanism. The characteristics of the floating mechanism have an important influence on the impact energy, frequency, and power of the hydraulic rock drill. Based on orifice throttling theory, the static equilibrium position of a damping piston was calculated, and the characteristic parameters of the double damping system were summarized as damping flow (Qd) and feed force (Fd). According to the characterization parameters of the double damping system, an experimental scheme of the floating characteristics of the double damping system was designed, and the combined experimental data of Qd and Fd were obtained. The 40 groups of experimental data were extracted, and the relationship between the combination (Qd, Fd) and impact energy and frequency and power were analyzed. The combination (Qd, Fd) with maximum and minimum power was selected to analyze the motion law of an impact piston. The maximum drilling power was obtained at the combination (Qd = 8 L·min−1, Fd = 16.25 kN). The influence factors of the double damping system on impact performance were summarized, and the characteristics of the double damping system under optimal impact performance were obtained.

Dynamic characteristics of hydrocarbon fuel within the channel at supercritical and pyrolysis condition

Regenerative cooling with fuel as the coolant is used in the scramjet engine. In order to grasp the dynamic characteristics of engine fuel supply processes, this article studies the dynamic characteristics of hydrocarbon fuel within the channel. A one-dimensional dynamic model was proved, the thermal energy storage effect, fuel volume effect and chemical dynamic effect have been considered in the model, the ordinary differential equations were solved using a 4th order Runge-Kutta method. The precision of the model was validated by three groups of experimental data. The effects of input signal, working condition, tube size on the dynamic characteristics of pressure, flow rate, temperature have been simulated. It is found that cracking reaction increased the compressibility of the fuel pyrolysis mixture and lead to longer responding time of outlet flow. The responding time of outlet flow can reach 3s when tube is 5m long which will greatly influence the control performance of the engine thrust system. Meanwhile, when the inlet flow rate appears the step change, the inlet pressure leads to overshoot, the overshoot can reach as much as 100%, such highly transient impulse will result in detrimental effect on fuel pump.

Fractional time-dependent apparent viscosity model for semisolid foodstuffs

The difficulty in the description of thixotropic behaviors in semisolid foodstuffs is the time dependent nature of apparent viscosity under constant shear rate. In this study, we propose a novel theoretical model via fractional derivative to address the high demand by industries. The present model adopts the critical parameter of fractional derivative order $\alpha$ to describe the corresponding time-dependent thixotropic behavior. More interestingly, the parameter $\alpha$ provides a quantitative insight into discriminating foodstuffs. With the re-exploration of three groups of experimental data (tehineh, balangu, and natillas), the proposed methodology is validated in good applicability and efficiency. The results show that the present fractional apparent viscosity model performs successfully for tested foodstuffs in the shear rate range of $50\mbox{--}150~\mbox{s}^{ - 1}$ . The fractional order $\alpha$ decreases with the increase of temperature at low temperature, below 50 °C, but increases with growing shear rate. While the ideal initial viscosity $k$ decreases with the increase of temperature, shear rate, and ingredient content. It is observed that the magnitude of $\alpha$ is capable of characterizing the thixotropy of semisolid foodstuffs.

MODELLING CHLORIDE DIFFUSION IN CONCRETE WITH INFLUENCE OF CONCRETE STRESS STATE

Under coastal or marine conditions, chloride erosion is the major accelerating factor of reinforcement corrosion. Therefore, it is of vital importance to investigate the chloride diffusion model. Research reveals that the concrete stress state has great influence on chloride diffusion; therefore a stress influence coefficient was incorporated in chloride diffusion coefficient model by many researchers. By referring to the experimental data from eight different researchers, the law between stress influence coefficient and concrete stress ratio is studied in detail, and equations relating the stress influence coefficient with the concrete stress ratio are established. Compared with three typical existing groups of equations, it is found that the proposed equations give the most accurate estim.ation of the stress influence coefficient. Hence, the proposed equations can be adopted to improve the valuation of chloride diffusion coefficient, and a modified chloride diffusion model is put forward. Three groups of experimental data are used to validate the modified chloride diffusion model, which is shown to be reasonable and having high prediction accuracy.

Effects on the Stability of the Balloon Shape in the Covered Yarn Process

In the process of producing covered yarn, a single outer wrapping yarn forms a balloon when it rotates at high speed. In this work, we used a mathematical model of a balloon formed by polyamide,which is a common outer wrapping yarn, and verified its feasibility with a group of experimental data. The effects of yarn tension, rotation speed, balloon height and turntable radius on the balloon shape were analysed and the correctness simultaneously demonstrated.

Identifying the shared metabolic objectives of glycerol bioconversion in Klebsiella pneumoniae under different culture conditions

This paper addresses the problem of identifying the shared metabolic objectives of glycerol bioconversion in Klebsiella pneumoniae for production of 1,3-propanediol (1,3-PD) under different culture conditions. To achieve this goal, we propose a multi-level programming model. This model includes three optimization problems, where the constraint region of the first level problem is implicitly determined by the other two optimization problems. The optimized objectives of the first and second level problems are to minimize the set of fluxes that are of major importance to glycerol metabolism and the difference between the observed fluxes and those computed by the model, respectively. The third level problem in the proposed multi-level programming simultaneously solves a set of flux balance analysis (FBA) models. A method is proposed to solve efficiently the presented multi-level programming problem. In this method, we first transform the proposed multi-level problem into a bi-level problem by applying the dual theory of linear programming to the FBA models of the third level. Next, the optimal solution of the above bi-level problem is obtained by iteratively solving a sequence of mixed integer programming problems. Optimization results reveal that the proposed method can identify the shared metabolic objectives of glycerol bioconversion in Klebsiella pneumoniae under three groups of experimental data.

Edge Detection in UAV Remote Sensing Images Using the Method Integrating Zernike Moments with Clustering Algorithms

Due to the unmanned aerial vehicle remote sensing images (UAVRSI) within rich texture details of ground objects and obvious phenomenon, the same objects with different spectra, it is difficult to effectively acquire the edge information using traditional edge detection operator. To solve this problem, an edge detection method of UAVRSI by combining Zernike moments with clustering algorithms is proposed in this study. To begin with, two typical clustering algorithms, namely, fuzzy c-means (FCM) and K-means algorithms, are used to cluster the original remote sensing images so as to form homogeneous regions in ground objects. Then, Zernike moments are applied to carry out edge detection on the remote sensing images clustered. Finally, visual comparison and sensitivity methods are adopted to evaluate the accuracy of the edge information detected. Afterwards, two groups of experimental data are selected to verify the proposed method. Results show that the proposed method effectively improves the accuracy of edge information extracted from remote sensing images.

Analysis of the Diffractive Deep Inelastic Scattering Data with Running Coupling and Gluon Number Fluctuations**Supported by the National Natural Science Foundation of China under Grant Nos 11305040, 11505036 and 11447203, the Education Department of Guizhou Province Talent Fund under Grant No [2015]5508, and the Science and Technology Department of Guizhou Province Fund under Grant Nos [2015]2114 and [2014]7053.

We study the effects of running coupling and gluon number fluctuations in the latest diffractive deep inelastic scattering data. It is found that the description of the data is improved once the running coupling and gluon number fluctuations are included with χ2 /d.o.f. = 0.867, χ2 /d.o.f. = 0.923 and χ2/d.o.f. = 0.878 for three different groups of experimental data. The values of diffusive coefficient subtracted from the fit are smaller than the ones obtained by considering only the gluon number fluctuations in our previous studies. The smaller values of the diffusive coefficient are in agreement with the theoretical predictions, where the gluon number fluctuations are suppressed by the running coupling which leads to smaller values of the diffusive coefficient.

Mechanistic Model for Nanoparticle Retention in Porous Media

With sizes larger than molecules but smaller than colloidal particles, nanoparticles exhibit unique transport properties in porous media. They can easily pass through typical pore throats in reservoir formations with micron diameters, but may get retained by physicochemical interaction with the pore walls. Based on detailed analysis of nanoparticle retention data from an extensive series of transport experiments, we examine the limitations of classical models of transport and interaction with a stationary phase. Some features of nanoparticle transport and retention are similar to those of adsorbing/desorbing solutes, while others are similar to those of depositing colloids. But neither solute sorption nor colloid filtration alone can explain all nanoparticle retention features, and of particular importance for subsurface applications, neither model can predict the effect of changing flow conditions on nanoparticle retention. The model that accounts for most observations is an independent two-site model which postulates physically independent sites of fixed capacity: one for reversible attachment and the other for irreversible attachment. We validate the model against five distinctly different groups of experimental data from the literature, through a rigorous approach of obtaining the model parameters from one experiment and blind testing against data from other experiments when experimental conditions vary.

Method for assessing discharge in meandering compound channels

Laboratory experiments were conducted in a large-scale meandering compound channel to investigate the flow patterns at the apex and cross-over sections. An equation in curvilinear co-ordinates was derived from the momentum equation and the flow continuity equation. In this equation, two shear stress terms are ignored due to their negligible contributions such that simple expressions are obtained. Several groups of experimental data from the published literature were used to verify this model. Comparison of the experimental and predictive results indicates that the proposed method is capable of accurately forecasting the stage discharge in meandering compound channels. Finally, the simple analytical solution of this model and the velocity parameter are further discussed.

Dynamic Heat Supply Prediction Using Support Vector Regression Optimized by Particle Swarm Optimization Algorithm

We developed an effective intelligent model to predict the dynamic heat supply of heat source. A hybrid forecasting method was proposed based on support vector regression (SVR) model-optimized particle swarm optimization (PSO) algorithms. Due to the interaction of meteorological conditions and the heating parameters of heating system, it is extremely difficult to forecast dynamic heat supply. Firstly, the correlations among heat supply and related influencing factors in the heating system were analyzed through the correlation analysis of statistical theory. Then, the SVR model was employed to forecast dynamic heat supply. In the model, the input variables were selected based on the correlation analysis and three crucial parameters, including the penalties factor, gamma of the kernel RBF, and insensitive loss function, were optimized by PSO algorithms. The optimized SVR model was compared with the basic SVR, optimized genetic algorithm-SVR (GA-SVR), and artificial neural network (ANN) through six groups of experiment data from two heat sources. The results of the correlation coefficient analysis revealed the relationship between the influencing factors and the forecasted heat supply and determined the input variables. The performance of the PSO-SVR model is superior to those of the other three models. The PSO-SVR method is statistically robust and can be applied to practical heating system.